Pyroptosis executive protein GSDMD as a biomarker for diagnosis and identification of Alzheimer’s disease
Bibliographic record
Abstract
Abstract Objective This study was mainly conducted to explore the expression changes of GSDMD and conventional markers (including T‐Tau, Tau181p, and Aβ 1–42 ) in the cerebrospinal fluid among patients with Alzheimer's disease (AD) and vascular dementia (VD), followed by determination of role of GSDMD in diagnosing and identifying AD and VD. Methods In this study, 60 patients with VD, 60 patients with AD, and 50 healthy controls were enrolled. Lumbar puncture was performed to collect cerebrospinal fluid samples. Patients with VD and patients with AD were evaluated using the Mini‐Mental State Examination (MMSE) scale, Montreal Cognitive Assessment (MoCA) scale, Clinical Dementia Rating (CDR) scale, Activity of Daily Living (ADL) scale, and Neuropsychiatric Inventory (NPI) questionnaire, aiming to determine the behavioral ability of patients. ELISA kit was purchased to determine the levels of GSDMD, T‐Tau, Tau181p, and Aβ 1–42 in cerebrospinal fluid, and the expression of inflammatory factors, IL‐1β and IL‐6, was also detected. Results (1) The levels of GSDMD, T‐Tau, and Tau181p in the cerebrospinal fluid were higher in patients with AD than those of patients with VD and healthy controls, while the levels of Aβ 1‐42 in the cerebrospinal fluid were lower in patients with AD than that in healthy controls and patients with VD. (2) GSDMD had good diagnostic accuracy in AD. Additionally, GSDMD, T‐Tau, Tau181p, and Aβ 1‐42 had good discrimination accuracy in distinguishing AD and VD. (3) The expression levels of inflammatory factors (IL‐1β and IL‐6) in cerebrospinal fluid were higher in patients with AD than those of healthy controls and patients with VD, which were positively correlated with GSDMD expression. Conclusion The expression of GSDMD was increased in patients with AD, which could be used as a biomarker for AD diagnosis and identification from VD.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".